Content based selection and meta tagging of advertisement breaks

ABSTRACT

A system evaluates stimulus materials such as videos, imagery, web pages, text, etc., in order to determine resonance and priming levels for various products and services at different temporal and spatial locations including advertisement breaks in the stimulus materials. The stimulus materials are tagged with resonance and priming level information to allow intelligent selection of suitable advertisement content for insertion at various locations in the stimulus materials. Response data such as survey data and/or neuro-response data including Event Related Potential (ERP), Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding data may be used to determine resonance and priming levels.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to Provisional Patent Application60/968,567, (Docket No. 2007NF16) titled Content Based Selection AndMeta-tagging Of Advertisement Breaks, by Anantha Pradeep, Robert T.Knight, and Ramachandran Gurumoorthy, and filed on Aug. 29, 2007, theentirety of which is incorporated by reference for all purposes.

TECHNICAL FIELD

The present disclosure relates to content based selection andmeta-tagging of advertisement breaks.

DESCRIPTION OF RELATED ART

Conventional systems for content selection and meta-tagging ofadvertisement breaks are limited or non-existent. Some conventionalsystems provide very rudimentary information for content selectionthrough demographic information and statistical data. However,conventional systems are subject to semantic, syntactic, metaphorical,cultural, and interpretive errors.

Consequently, it is desirable to provide improved methods and apparatusfor content selection and meta-tagging of advertisement (ad) breaks.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1 illustrates one example of a system for content selection andmeta-tagging of advertisement breaks.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with thecontent selection and meta-tagging system.

FIG. 5 illustrates one example of a report generated using the contentselection and meta-tagging system.

FIG. 6 illustrates one example of a technique for performing dataanalysis.

FIG. 7 illustrates one example of technique for content selection andmeta-tagging of advertisement breaks.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.

For example, the techniques and mechanisms of the present invention willbe described in the context of particular types of data such as centralnervous system, autonomic nervous system, and effector data. However, itshould be noted that the techniques and mechanisms of the presentinvention apply to a variety of different types of data. It should benoted that various mechanisms and techniques can be applied to any typeof stimuli. In the following description, numerous specific details areset forth in order to provide a thorough understanding of the presentinvention. Particular example embodiments of the present invention maybe implemented without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present invention.

Various techniques and mechanisms of the present invention willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present inventionunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present invention will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

A system evaluates stimulus materials such as videos, imagery, webpages, text, etc., in order to determine resonance and priming levelsfor various products and services at different temporal and spatiallocations including advertisement breaks in the stimulus materials. Thestimulus materials are tagged with resonance and priming levelinformation to allow intelligent selection of suitable advertisementcontent for insertion at various locations in the stimulus materials.Response data such as survey data and/or neuro-response data includingEvent Related Potential (ERP), Electroencephalography (EEG), GalvanicSkin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG),eye tracking, and facial emotion encoding data may be used to determineresonance and priming levels.

Example Embodiments

Conventional mechanisms for selecting advertising content rely ondemographic information, statistical information, and survey basedresponse collection. One problem with conventional mechanisms forselecting advertising is that they do not measure the inherent messageresonance and priming for various products, services, and offerings thatare attributable to the stimulus. They are also prone to semantic,syntactic, metaphorical, cultural, and interpretive errors therebypreventing the accurate and repeatable targeting of the audience.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus. Conventional systems alsofail to blend multiple datasets, and blended manifestations ofmulti-modal responses, across multiple datasets, individuals andmodalities, to reveal and validate the elicited measures of resonanceand priming to allow for intelligent selection of advertising content.

In these respects, the content selection and meta-tagging of advertisingbreak system according to the present invention substantially departsfrom the conventional concepts and designs of the prior art. Accordingto various embodiments, it is recognized that advertisements forparticular products, services, and offerings may be particularlyeffective when a subject is primed for the particular products,services, and offerings. For examples, an advertisement for cleaningsupplies may be particularly effective after viewers watch a sceneshowing a dirty room, or an advertisement for a fuel efficient car maybe particularly effective after viewers watch a documentary about highoil prices. In still other examples, an audio advertisement for packagedsalads may be more effective after viewers hear a radio program aboutcoronary disease, or a brand image for camping products may be moreeffective placed near a mural showing mountain scenery.

Consequently, the techniques and mechanisms of the present invention tagstimulus materials such as video, audio, web pages, printed materials,etc. with information indicating resonance and/or priming levels forvarious products, services and offerings. Meta-tags may be stored in aseparate repository or in the stimulus material itself. In someexamples, advertising content suitable for particular priming levels maybe automatically selected based on meta-tags for introduction into thestimulus materials. In other examples, advertising content can beintelligently inserted based on priming levels for particular productsand services. In some examples, advertising break slots can be sold orauctioned more efficiently based on priming levels and resonance.Advertisers can assess the value of particular slots based on priminglevels and resonance.

According to various embodiments, the techniques and mechanisms of thepresent invention may use a variety of mechanisms such as survey basedresponses, statistical data, and/or neuro-response measurements such ascentral nervous system, autonomic nervous system, and effectormeasurements to improve content selection and meta-tagging of stimulusmaterial. Some examples of central nervous system measurement mechanismsinclude Functional Magnetic Resonance Imaging (fMRI) andElectroencephalography (EEG). fMRI measures blood oxygenation in thebrain that correlates with increased neural activity. However, currentimplementations of FMRI have poor temporal resolution of few seconds.EEG measures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Even portable EEG with dry electrodes provides alarge amount of neuro-response information.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various embodiments, the techniques and mechanisms of thepresent invention intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform content selection and meta-tagging. In some examples, autonomicnervous system measures are themselves used to validate central nervoussystem measures. Effector and behavior responses are blended andcombined with other measures. According to various embodiments, centralnervous system, autonomic nervous system, and effector systemmeasurements are aggregated into a measurement that allows contentselection and meta-tagging of advertising breaks.

In particular embodiments, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousembodiments, data is collected in order to determine a resonance measurethat aggregates multiple component measures that assess resonance data.In particular embodiments, specific event related potential (ERP)analyses and/or event related power spectral perturbations (ERPSPs) areevaluated for different regions of the brain both before a subject isexposed to stimulus and each time after the subject is exposed tostimulus.

Pre-stimulus and post-stimulus differential as well as target anddistracter differential measurements of ERP time domain components atmultiple regions of the brain are determined (DERP). Event relatedtime-frequency analysis of the differential response to assess theattention, emotion and memory retention (DERPSPs) across multiplefrequency bands including but not limited to theta, alpha, beta, gammaand high gamma is performed. In particular embodiments, single trialand/or averaged DERP and/or DERPSPs can be used to enhance the resonancemeasure and determine priming levels for various products and services.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc. can be analyzed.According to various embodiments, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various embodiments, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present invention recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, and other abilities are not merely based on one part ofthe brain but instead rely on network interactions between brainregions.

The techniques and mechanisms of the present invention further recognizethat different frequency bands used for multi-regional communication canbe indicative of the effectiveness of stimuli. In particularembodiments, evaluations are calibrated to each subject and synchronizedacross subjects. In particular embodiments, templates are created forsubjects to create a baseline for measuring pre and post stimulusdifferentials. According to various embodiments, stimulus generators areintelligent and adaptively modify specific parameters such as exposurelength and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively perform content selection andmeta-tagging.

FIG. 1 illustrates one example of a system for performing contentselection and meta-tagging using central nervous system, autonomicnervous system, and/or effector measures. According to variousembodiments, the content selection and meta-tagging system includes astimulus presentation device 101. In particular embodiments, thestimulus presentation device 101 is merely a display, monitor, screen,etc., that displays stimulus material to a user. The stimulus materialmay be a media clip, a commercial, pages of text, a brand image, aperformance, a magazine advertisement, a movie, an audio presentation,and may even involve particular tastes, smells, textures and/or sounds.The stimuli can involve a variety of senses and occur with or withouthuman supervision. Continuous and discrete modes are supported.According to various embodiments, the stimulus presentation device 101also has protocol generation capability to allow intelligentcustomization of stimuli provided to multiple subjects in differentmarkets.

According to various embodiments, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various embodiments, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, GSR, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various embodiments, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular embodiments, the data collection devices 105 includeEEG 111, EOG 113, and GSR 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular embodiments, datacollected is digitally sampled and stored for later analysis. Inparticular embodiments, the data collected could be analyzed inreal-time. According to particular embodiments, the digital samplingrates are adaptively chosen based on the neurophysiological andneurological data being measured.

In one particular embodiment, the content selection and meta-taggingsystem includes EEG 111 measurements made using scalp level electrodes,EOG 113 measurements made using shielded electrodes to track eye data,GSR 115 measurements performed using a differential measurement system,a facial muscular measurement through shielded electrodes placed atspecific locations on the face, and a facial affect graphic and videoanalyzer adaptively derived for each individual.

In particular embodiments, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularembodiments, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousembodiments, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, or a sceneoutside a window. The data collected allows analysis of neuro-responseinformation and correlation of the information to actual stimulusmaterial and not mere subject distractions.

According to various embodiments, the content selection and meta-taggingsystem also includes a data cleanser device 121. In particularembodiments, the data cleanser device 121 filters the collected data toremove noise, artifacts, and other irrelevant data using fixed andadaptive filtering, weighted averaging, advanced component extraction(like PCA, ICA), vector and component separation methods, etc. Thisdevice cleanses the data by removing both exogenous noise (where thesource is outside the physiology of the subject, e.g. a phone ringingwhile a subject is viewing a video) and endogenous artifacts (where thesource could be neurophysiological, e.g. muscle movements, eye blinks,etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various embodiments, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before data analyzer 181, the datacleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoeverwhile in other systems, data cleanser devices may be integrated intoindividual data collection devices.

According to various embodiments, an optional data meta attributesrepository 131 provides information on the stimulus material beingpresented. According to various embodiments, stimulus attributes includeproperties of the stimulus materials as well as purposes, presentationattributes, report generation attributes, etc. In particularembodiments, stimulus attributes include time span, channel, rating,media, type, etc. Stimulus attributes may also include positions ofentities in various frames, object relationships, locations of objectsand duration of display. Purpose attributes include aspiration andobjects of the stimulus including excitement, memory retention,associations, etc. Presentation attributes include audio, video,imagery, and messages needed for enhancement or avoidance. Otherattributes may or may not also be included in the stimulus attributesrepository or some other repository.

The data cleanser device 121 and the stimulus attributes repository 131pass data to the data analyzer 181. The data analyzer 181 uses a varietyof mechanisms to analyze underlying data in the system to determineresonance. According to various embodiments, the data analyzercustomizes and extracts the independent neurological andneuro-physiological parameters for each individual in each modality, andblends the estimates within a modality as well as across modalities toelicit an enhanced response to the presented stimulus material. Inparticular embodiments, the data analyzer 181 aggregates the responsemeasures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various embodiments, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular embodiments, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularembodiments, the intra-modality response synthesizer also aggregatesdata from different subjects in a dataset.

According to various embodiments, the cross-modality responsesynthesizer or fusion device blends different intra-modality responses,including raw signals and signals output. The combination of signalsenhances the measures of effectiveness within a modality. Thecross-modality response fusion device can also aggregate data fromdifferent subjects in a dataset.

According to various embodiments, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular embodiments, blendedestimates are provided for each exposure of a subject to stimulusmaterials. The blended estimates are evaluated over time to assessresonance characteristics. According to various embodiments, numericalvalues are assigned to each blended estimate. The numerical values maycorrespond to the intensity of neuro-response measurements, thesignificance of peaks, the change between peaks, etc. Higher numericalvalues may correspond to higher significance in neuro-responseintensity. Lower numerical values may correspond to lower significanceor even insignificant neuro-response activity. In other examples,multiple values are assigned to each blended estimate. In still otherexamples, blended estimates of neuro-response significance aregraphically represented to show changes after repeated exposure.

According to various embodiments, the data analyzer 181 providesanalyzed and enhanced response data to a data communication device. Itshould be noted that in particular instances, a data communicationdevice is not necessary. According to various embodiments, the datacommunication device provides raw and/or analyzed data and insights. Inparticular embodiments, the data communication device may includemechanisms for the compression and encryption of data for secure storageand communication.

According to various embodiments, the data communication devicetransmits data using protocols such as the File Transfer Protocol (FTP),Hypertext Transfer Protocol (HTTP) along with a variety of conventional,bus, wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures. According to various embodiments, the data communicationdevice is a set top box, wireless device, computer system, etc. thattransmits data obtained from a data collection device to a resonanceestimator 185. In particular embodiments, the data communication devicemay transmit data even before data cleansing or data analysis. In otherexamples, the data communication device may transmit data after datacleansing and analysis.

In particular embodiments, the data communication device sends data to aresonance estimator 185. According to various embodiments, the resonanceestimator 185 assesses and extracts resonance patterns. In particularembodiments, the resonance estimator 185 determines entity positions invarious stimulus segments and matches position information with eyetracking paths while correlating saccades with neural assessments ofattention, memory retention, and emotional engagement. In particularembodiments, the resonance estimator 185 also collects and integratesuser behavioral and survey responses with the analyzed response data tomore effectively estimate resonance.

According to various embodiments, the resonance estimator 185 providesdata to a priming repository system 187. In particular embodiments, thepriming repository system 187 associates meta-tags with various temporaland spatial locations in stimulus material, such as a televisionprogram, movie, video, audio program, print advertisement, etc. In someexamples, every second of a show is associated with a set of meta-tags.In other examples, commercial or advertisement (ad) breaks are providedwith a set of meta-tags that identify commercial or advertising contentthat would be most suitable for a particular break.

Pre-break content may identify categories of products and services thatare primed at a particular point in a program. The content may alsospecify the level of priming associated with each category of product orservice. For example, a movie may show old house and buildings.Meta-tags may be manually or automatically generated to indicate thatcommercials for home improvement products would be suitable for aparticular advertisement break.

In some instances, meta-tags may include spatial and temporalinformation indicating where and when particular advertisements shouldbe placed. For example, a documentary about wildlife that shows a blankwall in several scenes may include meta-tags that indicate a banneradvertisement for nature oriented vacations may be suitable. Theadvertisements may be separate from a program or integrated into aprogram. According to various embodiments, the priming repository system187 also identifies scenes eliciting significant audience resonance toparticular products and services as well as the level and intensity ofresonance.

A variety of data can be stored for later analysis, management,manipulation, and retrieval. In particular embodiments, the repositorycould be used for tracking stimulus attributes and presentationattributes, audience responses optionally could also be integrated intometa-tags.

As with a variety of the components in the system, the repository can beco-located with the rest of the system and the user, or could beimplemented in a remote location. It could also be optionally separatedinto repository system that could be centralized or distributed at theprovider or providers of the stimulus material. In other examples, therepository system itself is integrated into a library of stimulusmaterials such as a media library.

FIG. 2 illustrates examples of data models that may be provided with astimulus attributes repository. According to various embodiments, astimulus attributes data model 201 includes a channel 203, media type205, time span 207, audience 209, and demographic information 211. Astimulus purpose data model 215 may include intents 217 and objectives219. According to various embodiments, stimulus attributes data model201 also includes spatial and temporal information 221 about entitiesand emerging relationships between entities.

According to various embodiments, another stimulus attributes data model221 includes creation attributes 223, ownership attributes 225,broadcast attributes 227, and statistical, demographic and/or surveybased identifiers 229 for automatically integrating theneuro-physiological and neuro-behavioral response with other attributesand meta-information associated with the stimulus.

According to various embodiments, a stimulus priming data model 231includes fields for identifying advertisement breaks 233 and scenes 235that can be associated with various priming levels 237 and audienceresonance measurements 239. In particular embodiments, the data model231 provides temporal and spatial information for ads, scenes, events,locations, etc. that may be associated with priming levels and audienceresonance measurements. In some examples, priming levels for a varietyof products, services, offerings, etc. are correlated with temporal andspatial information in source material such as a movie, billboard,advertisement, commercial, store shelf, etc. In some examples, the datamodel associates with each second of a show a set of meta-tags forpre-break content indicating categories of products and services thatare primed. The level of priming associated with each category ofproduct or service when the advertisement break is specified may also beprovided. Audience resonance measurements and maximal audience resonancemeasurements for various scenes and advertisement breaks may bemaintained and correlated with sets of products, services, offerings,etc.

The priming and resonance information may be used to select stimuluscontent suited for particular levels of priming and resonance. In someexamples, the priming and resonance information may be used to moreintelligently price advertising breaks based on value to advertisers.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement of resonance.According to various embodiments, a dataset data model 301 includes anexperiment name 303 and/or identifier, client attributes 305, a subjectpool 307, logistics information 309 such as the location, date, and timeof testing, and stimulus material 311 including stimulus materialattributes.

In particular embodiments, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

According to various embodiments, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various embodiments, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular embodiments, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various embodiments, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with content selection and meta-tagging. According tovarious embodiments, queries are defined from general or customizedscripting languages and constructs, visual mechanisms, a library ofpreset queries, diagnostic querying including drill-down diagnostics,and eliciting what if scenarios. According to various embodiments,subject attributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as testing times and dates,and demographic attributes 419. Demographics attributes includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may be used include thenumber of protocol repetitions used, combination of protocols used, andusage configuration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various embodiments, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andresonance measures 507. Effectiveness assessment measures includecomposite assessment measure(s), industry/category/client specificplacement (percentile, ranking, etc.), actionable grouping assessmentsuch as removing material, modifying segments, or fine tuning specificelements, etc, and the evolution of the effectiveness profile over time.In particular embodiments, component assessment reports includecomponent assessment measures like attention, emotional engagementscores, percentile placement, ranking, etc. Component profile measuresinclude time based evolution of the component measures and profilestatistical assessments. According to various embodiments, reportsinclude the number of times material is assessed, attributes of themultiple presentations used, evolution of the response assessmentmeasures over the multiple presentations, and usage recommendations.

According to various embodiments, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various embodiments, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular embodiments,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of content selection and meta-tagging. At601, stimulus material is provided to multiple subjects. According tovarious embodiments, stimulus includes streaming video and audio. Inparticular embodiments, subjects view stimulus in their own homes ingroup or individual settings. In some examples, verbal and writtenresponses are collected for use without neuro-response measurements. Inother examples, verbal and written responses are correlated withneuro-response measurements. At 603, subject neuro-response measurementsare collected using a variety of modalities, such as EEG, ERP, EOG, GSR,etc. At 605, data is passed through a data cleanser to remove noise andartifacts that may make data more difficult to interpret. According tovarious embodiments, the data cleanser removes EEG electrical activityassociated with blinking and other endogenous/exogenous artifacts.

According to various embodiments, data analysis is performed. Dataanalysis may include intra-modality response synthesis andcross-modality response synthesis to enhance effectiveness measures. Itshould be noted that in some particular instances, one type of synthesismay be performed without performing other types of synthesis. Forexample, cross-modality response synthesis may be performed with orwithout intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular embodiments, a stimulus attributes repository 131 is accessedto obtain attributes and characteristics of the stimulus materials,along with purposes, intents, objectives, etc. In particularembodiments, EEG response data is synthesized to provide an enhancedassessment of effectiveness. According to various embodiments, EEGmeasures electrical activity resulting from thousands of simultaneousneural processes associated with different portions of the brain. EEGdata can be classified in various bands. According to variousembodiments, brainwave frequencies include delta, theta, alpha, beta,and gamma frequency ranges. Delta waves are classified as those lessthan 4 Hz and are prominent during deep sleep. Theta waves havefrequencies between 3.5 to 7.5 Hz and are associated with memories,attention, emotions, and sensations. Theta waves are typically prominentduring states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present inventionrecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular embodiments, EEGmeasurements including difficult to detect high gamma or kappa bandmeasurements are obtained, enhanced, and evaluated. Subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various embodiments, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalograophy) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various embodiments of the present invention recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular embodiments,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular embodiments,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various embodiments,the synchronous response may be determined for multiple subjectsresiding in separate locations or for multiple subjects residing in thesame location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious embodiments, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular embodiments, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present invention recognize that significance measurescan be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousembodiments, a cross-modality synthesis mechanism performs time andphase shifting of data to allow data from different modalities to align.In some examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various embodiments, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various embodiments, ERP measures are enhancedusing EEG time-frequency measures (ERPSP) in response to thepresentation of the marketing and entertainment stimuli. Specificportions are extracted and isolated to identify ERP, DERP and ERPSPanalyses to perform. In particular embodiments, an EEG frequencyestimation of attention, emotion and memory retention (ERPSP) is used asa co-factor in enhancing the ERP, DERP and time-domain responseanalysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousembodiments, EOG and eye tracking is enhanced by measuring the presenceof lambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular embodiments, specific EEGsignatures of activity such as slow potential shifts and measures ofcoherence in time-frequency responses at the Frontal Eye Field (FEF)regions that preceded saccade-onset are measured to enhance theeffectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various embodiments, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various embodiments, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present invention recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various embodiments, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to anadvertisement or the brand response attributable to multiple brands isdetermined using pre-resonance and post-resonance estimates

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 611, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various embodiments, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa. At 613, priming levels and resonance for variousproducts, services, and offerings are determined at different locationsin the stimulus material. In some examples, priming levels and resonanceare manually determined. In other examples, priming levels and resonanceare automatically determined using neuro-response measurements.According to various embodiments, video streams are modified withdifferent inserted advertisements for various products and services todetermine the effectiveness of the inserted advertisements based onpriming levels and resonance of the source material.

At 617, multiple trials are performed to enhance priming and resonancemeasures. In some examples, stimulus. In some examples, multiple trialsare performed to enhance resonance measures.

In particular embodiments, the priming and resonance measures are sentto a priming repository 619. The priming repository 619 may be used toautomatically select advertising suited for particular ad breaks.

FIG. 7 illustrates an example of a technique for estimating resonance.According to various embodiments, measurements from different modalitiesare obtained. According to various embodiments, measurements includingDifferential Event Related Potential (DERP), Differential Event RelatedPower Spectral Perturbations (DERPSPs), Pupilary Response, etc., areblended to obtain a combined measurement. In particular embodiments,each measurement may have to be aligned appropriately in order to allowblending. According to various embodiments, a resonance estimatorincludes mechanisms to use and blend different measures from across themodalities from the data analyzer. In particular embodiments, the dataincludes the DERP measures, DERPSPs, pupilary response, GSR, eyemovement, coherence, coupling and lambda wave based response.Measurements across modalities are blended to elicit a synthesizedmeasure of user resonance.

In particular embodiments, user resonance to attributes of stimulusmaterial such as communication, concept, experience, message, images,genre, product categories, service categories, etc. are measured at 701.The attributes of the stimulus material are evaluated to identify adcategories and genres that are naturally primed as a consequence of thepreceding content 703. The effectiveness of source material may bedetermined using a mechanism to weigh and combine the outputs of thedata analyzer. According to various embodiments, a set of predeterminedweights and nonlinear functions combine the outputs of the data analyzerto determine a hierarchy of the effectiveness of a set of categories forproducts and services that are primed by the source material orpre-break show content at 705. According to various embodiments, a setof predetermined weights and nonlinear functions combine the outputs ofthe data analyzer to determine scenes of a show of maximal effectivenessto perform a differential extraction of categories of products andservices that are effectively primed by the source material at 707.

At 711, priming levels for various products and services are correlatedwith various ad breaks based on the number of scenes of maximaleffectiveness, the number of categories of products and services primedby the pre break content, and the level of priming effectiveness foreach category.

At 713, priming levels and resonance are maintained in a priming levelrepository. In some examples, the priming levels and resonance arewritten to the source material.

According to various embodiments, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a resonance measurement system.

According to particular example embodiments, a system 800 suitable forimplementing particular embodiments of the present invention includes aprocessor 801, a memory 803, an interface 811, and a bus 815 (e.g., aPCI bus). When acting under the control of appropriate software orfirmware, the processor 801 is responsible for such tasks such aspattern generation. Various specially configured devices can also beused in place of a processor 801 or in addition to processor 801. Thecomplete implementation can also be done in custom hardware. Theinterface 811 is typically configured to send and receive data packetsor data segments over a network. Particular examples of interfaces thedevice supports include host bus adapter (HBA) interfaces, Ethernetinterfaces, frame relay interfaces, cable interfaces, DSL interfaces,token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular example embodiments, the system 800 uses memory803 to store data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

1. A system, comprising: a stimulus presentation device operable toprovide stimulus material to a subject; a data collection deviceoperable to obtain response data from the subject exposed to thestimulus material; a resonance estimator operable to identify attributesof the stimulus materials and determine subject resonance to thestimulus material and attributes of the stimulus material, whereinsubject resonance is used to determine priming levels for a plurality ofproducts and services; a priming repository system operable to correlatepriming levels for a plurality of products and services to variouspositions in the stimulus material, wherein the various positionsinclude advertisement breaks.
 2. The system of claim 1, wherein responsedata is neuro-response data.
 3. The system of claim 1, wherein responsedata is survey data.
 4. The system of claim 1, wherein the datacollection device is further operable to obtain target and distracterERP measurements to determine differential measurements of ERP timedomain components at multiple regions of the brain (DERP).
 5. The systemof claim 1, wherein the data collection device is further operable toobtain event related time-frequency analysis of the differentialresponse to assess the attention, emotion and memory retention (DERPSPs)across multiple frequency bands.
 6. The system of claim 5, wherein themultiple frequency bands comprise theta, alpha, beta, gamma and highgamma.
 7. The system of claim 1, wherein attributes of the stimulusmaterial comprise communication, concept, experience, message, images,audio, pricing, packaging.
 8. The system of claim 1, wherein the subjectresonance measurement is used to identify advertising breaks foradditional marketing, advertising and otheraudio/visual/tactile/olfactory stimulus.
 9. The system of claim 1,further comprising a data analyzer operable to determine the DERP. 10.The system of claim 1, wherein the data collection device is furtheroperable to obtain pupillary dilation, galvanic skin response (GSR), andheart rate measurements for the subject.
 11. The system of claim 2,wherein neuro-response data includes central nervous system andautonomic nervous system data.
 12. The system of claim 2, whereinneuro-response data includes central nervous system and effector data.13. The system of claim 2, wherein combinations of neurological andneurophysiological measurements including attention, emotion, and memoryretention are used to perform content selection and meta-tagging.
 14. Amethod, comprising: presenting stimulus material to a subject; obtainingresponse data from the subject exposed to the stimulus material;identifying attributes of the stimulus materials and determine subjectresonance measurements for the stimulus material and attributes of thestimulus material; determining priming levels for a plurality ofproducts and services using the subject resonance measurements;correlating priming levels for a plurality of products and services tovarious positions in the stimulus material, wherein the variouspositions include advertisement breaks.
 15. The method of claim 14,wherein response data is neuro-response data.
 16. The method of claim14, wherein the data collection device is further operable to obtaintarget and distracter ERP measurements to determine differentialmeasurements of ERP time domain components at multiple regions of thebrain (DERP).
 17. The method of claim 14, wherein the data collectiondevice is further operable to obtain event related time-frequencyanalysis of the differential response to assess the attention, emotionand memory retention (DERPSPs) across multiple frequency bands.
 18. Themethod of claim 17, wherein the multiple frequency bands comprise theta,alpha, beta, gamma and high gamma.
 19. The method of claim 14, whereinattributes of the stimulus material comprise communication, concept,experience, message, images, audio, pricing, packaging.
 20. The methodof claim 14, wherein the subject resonance measurement is used toidentify advertising breaks for additional marketing, advertising andother audio/visual/tactile/olfactory stimulus.
 21. An apparatus,comprising: means for presenting stimulus material to a subject; meansfor obtaining response data from the subject exposed to the stimulusmaterial; means for identifying attributes of the stimulus materials anddetermine subject resonance measurements for the stimulus material andattributes of the stimulus material; means for determining priminglevels for a plurality of products and services using the subjectresonance measurements; means for correlating priming levels for aplurality of products and services to various positions in the stimulusmaterial, wherein the various positions include advertisement breaks.